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Authentic Oracle 1z0-1127-24 Exam Questions & Answers

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Oracle 1z0-1127-24 Exam Syllabus Topics:

Topic
Details

Topic 1

Fundamentals of Large Language Models (LLMs): For AI developers and Cloud Architects, this topic discusses LLM architectures and LLM fine-tuning. Additionally, it focuses on prompts for LLMs and fundamentals of code models.

Topic 2

Using OCI Generative AI Service: For AI Specialists, this section covers dedicated AI clusters for fine-tuning and inference. The topic also focuses on the fundamentals of OCI Generative AI service, foundational models for Generation, Summarization, and Embedding.

Topic 3

Building an LLM Application with OCI Generative AI Service: For AI Engineers, this section covers Retrieval Augmented Generation (RAG) concepts, vector database concepts, and semantic search concepts. It also focuses on deploying an LLM, tracing and evaluating an LLM, and building an LLM application with RAG and LangChain.

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q37-Q42):

NEW QUESTION # 37
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?

A. Specifies a string that tells the model to stop generating more content
B. Assigns a penalty to tokens that have already appeared in the preceding text
C. Controls the randomness of the model's output, affecting its creativity
D. Determines the maximum number of tokens the model can generate per response

Answer: C

NEW QUESTION # 38
How does the Retrieval-Augmented Generation (RAG) Token technique differ from RAG Sequence when generating a model's response?

A. RAG Token retrieves documents oar/at the beginning of the response generation and uses those for the entire content
B. RAG Token does not use document retrieval but generates responses based on pre-existing knowledge only.
C. Unlike RAG Sequence, RAG Token generates the entire response at once without considering individual parts.
D. RAG Token retrieves relevant documents for each part of the response and constructs the answer incrementally.

Answer: A

NEW QUESTION # 39
Which is a key characteristic of the annotation process used in T-Few fine-tuning?

A. T-Few fine-tuning requires manual annotation of input-output pain.
B. T-Few fine-tuning uses annotated data to adjust a fraction of model weights.
C. T-Few fine-tuning relies on unsupervised learning techniques for annotation.
D. T- Few fine-tuning involves updating the weights of all layers in the model.

Answer: C

NEW QUESTION # 40
How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?

A. Stored in an unencrypted form in Object Storage
B. Shared among multiple customers for efficiency
C. Stored in Object Storage encrypted by default
D. Stored in Key Management service

Answer: C

Explanation:
Fine-tuned customer models in the OCI Generative AI service are stored in Object Storage, and they are encrypted by default. This encryption ensures strong data privacy and security by protecting the model data from unauthorized access. Using encrypted storage is a key measure in safeguarding sensitive information and maintaining compliance with security standards.
Reference
OCI documentation on data storage and security practices
Technical details on encryption and data privacy in OCI services

NEW QUESTION # 41
Which is a key advantage of usingT-Few over Vanilla fine-tuning in the OCI Generative AI service?

A. Enhanced generalization to unseen data
B. Increased model interpretability
C. Foster training time and lower cost
D. Reduced model complexity

Answer: C

NEW QUESTION # 42
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